code: def __init__(self, num_classes, **kwargs):
      super(InceptionAux, self).__init__(**kwargs)
      self.averagePool = layers.AvgPool2D(pool_size=5, strides=3)
      self.conv = layers.Conv2D(
          128, kernel_size=1, use_bias=False, name="conv/conv"
      )
      self.bn1 = layers.BatchNormalization(
          momentum=0.9, epsilon=1e-5, name="conv/bn"
      )
      self.rule1 = layers.ReLU()
  
      self.fc1 = layers.Dense(1024, activation="relu", name="fc1")
      self.fc2 = layers.Dense(num_classes, name="fc2")
      self.softmax = layers.Softmax()
  
file_path: deep-learning-for-image-processing/tensorflow_classification/Test4_goolenet/model_add_bn.py
imports: from tensorflow.keras import layers, models, Model, Sequential
label_desc: Use image node_id 1 to create a new InceptionAux object from inherited base classes: layers with example: obj = InceptionAux(num_classes)